Josep
Roure Alcobé
Contact: roure_at_eupmt_dot_cat
Research Interests
My main
research
interest are Machine Learning and Data Mining, the exciting world of
building models and finding patterns from data. My work is focused on
improving learning algorithms and data structures. I also enjoy
applying
Machine Learning techniques in Biosurveillance (detecting rare events
in datastreams) and
in Biometric recognition:
- Incremental
algorithms:
Nowadays, in most challenging applications of machine learning data
flows continuously, even in infinite datastreams. The ability to
incorporate new information in a decision model, is a desirable
property of any learning algorithm. I worked on incremental Clustering
(in a joint work with Luis
Talavera) and in incremental Bayesian Network learning during
my PhD (advisor Ramon
Sangüesa).
- Data mining algorithms rely on data structures in
order to reduce the time and memory needed to process very
large amounts of data. For example structures for catching sufficient
statistics. I worked on an incremental version of AD-trees (a join work
with Andrew W.
Moore in the Auton
Lab, CMU).
- In Biosurveillance,
Machine Learning and Data Mining can help in timely and
reialibly detecting potential infectious disease
outbreaks.
Great quantities of animal and public health infectious disease and
surveillance data are collected by various laboratories, health care
providers, veterinarians, and government agencies. I applied Machine
Learning techniques in order to combine information evidence from
multiple streams of surveillance and improve timeliness and
specificity of detection of outbreaks (a joint work with Artur W.
Dubrawski and Jeff
Schneider in the Auton Lab, CMU).
- Biometric
recognition
refers to a science that studies statistical methods to uniquely
recognize humans based upon one or more physical (face, fingerprints,
...) or behavioral (speech, signature...) traits. I work in face and
signature recognition (a joint work with Marcos Faundez and Joan
Fàbregas).
Take a look at my resumee
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